On a Rough Sets Based Tool for Generating Rules from Data with Categorical and Numerical Values

Rough set theory has mainly been applied to data with categorical values. In order to handle data with numerical values, we have defined numerical patterns with two symbols # and @, and have proposed more flexible rough sets based rule generation. The concepts of `coarse' and `fine' for rules are explicitly defined according to numerical patterns. This paper focuses on the rough sets based method for rule generation, which is enhanced by numerical patterns, and refers to the tool programs. Tool programs are applied to data in UCI Machine Learning Repository, and some useful rules are obtained.

[1]  Zdzisław Pawlak,et al.  Rough set theory and its applications , 2002, Journal of Telecommunications and Information Technology.

[2]  Jerzy W. Grzymala-Busse,et al.  Global discretization of continuous attributes as preprocessing for machine learning , 1996, Int. J. Approx. Reason..

[3]  Hiroshi Sakai,et al.  Basic Algorithms and Tools for Rough Non-deterministic Information Analysis , 2004, Trans. Rough Sets.

[4]  law Pawlak.  Some Issues on Rough Sets Zdzis , 2004 .

[5]  Zdzislaw Pawlak,et al.  Some Issues on Rough Sets , 2004, Trans. Rough Sets.

[6]  Hiroshi Sakai.  Effective Procedures for Handling Possible Equivalence Relations in Non-deterministic Information Systems , 2001, Fundam. Informaticae.

[7]  Lech Polkowski,et al.  Rough Sets in Knowledge Discovery 2 , 1998 .

[8]  Janusz Zalewski,et al.  Rough sets: Theoretical aspects of reasoning about data , 1996 .

[9]  J. Grzymala-Busse,et al.  Three discretization methods for rule induction , 2001 .

[10]  Heikki Mannila,et al.  Fast Discovery of Association Rules , 1996, Advances in Knowledge Discovery and Data Mining.

[11]  Jerzy W. Grzymala-Busse,et al.  Transactions on Rough Sets I , 2004, Lecture Notes in Computer Science.

[12]  Andrzej Skowron,et al.  The Discernibility Matrices and Functions in Information Systems , 1992, Intelligent Decision Support.

[13]  Tetsuya Murai,et al.  On a Tool for Rough Non-deterministic Information Analysis and Its Perspective for Handling Numerical Data , 2005, MDAI.

[14]  Germano Resconi,et al.  Granular Reasoning Using Zooming In & Out , 2003, RSFDGrC.

[15]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.

[16]  Shusaku Tsumoto,et al.  Foundations of Intelligent Systems, 15th International Symposium, ISMIS 2005, Saratoga Springs, NY, USA, May 25-28, 2005, Proceedings , 2005, ISMIS.

[17]  Yiyu Yao,et al.  Granular Computing Based on Rough Sets, Quotient Space Theory, and Belief Functions , 2003, ISMIS.

[18]  Stephen Watson,et al.  Set Theory and its Applications , 1989 .

[19]  Germano Resconi,et al.  Granular reasoning using zooming in & out. Part 1. Propositional reasoning (an extended abstract) , 2003 .

[20]  Hiroshi Sakai,et al.  Discernibility Functions and Minimal Rules in Non-deterministic Information Systems , 2005, RSFDGrC.

[21]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.